## [1] 0.03980539
## [1] 2.361799
## # A tibble: 10 x 2
## t r_effective
## <dbl> <dbl>
## 1 1 2.5
## 2 2 2.46
## 3 3 2.36
## 4 4 2.36
## 5 5 2.36
## 6 6 2.36
## 7 7 2.36
## 8 8 2.36
## 9 9 2.36
## 10 10 2.36
## # A tibble: 42 x 20
## r_effective prop_identified alpha R kappa eta nu t_ds t_da t_qcs
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
## 1 2.41 0.0654 0.2 2.5 0.5 0.5 4 1 3 3
## 2 2.11 0.271 0.2 2.5 0.5 0.5 4 1 3 3
## 3 1.72 0.509 0.2 2.5 0.5 0.5 4 1 3 3
## 4 2.42 0.0680 0.2 2.5 0.5 0.5 4 2 3 3
## 5 2.14 0.281 0.2 2.5 0.5 0.5 4 2 3 3
## 6 1.79 0.522 0.2 2.5 0.5 0.5 4 2 3 3
## 7 2.43 0.0699 0.2 2.5 0.5 0.5 4 3 3 3
## 8 2.17 0.288 0.2 2.5 0.5 0.5 4 3 3 3
## 9 1.84 0.529 0.2 2.5 0.5 0.5 4 3 3 3
## 10 2.43 0.0711 0.2 2.5 0.5 0.5 4 4 3 3
## # … with 32 more rows, and 10 more variables: t_qca <dbl>, t_qhs <dbl>,
## # t_qha <dbl>, t_q <dbl>, omega_c <dbl>, omega_h <dbl>, omega_q <dbl>,
## # quarantine_days <dbl>, rho_s <dbl>, rho_a <dbl>
grid <- grid %>%
mutate(
rho_a = pmin(rho_s * 0.5, 1),
t_da = t_ds,
t_qcs = t_ds,
t_qca = t_ds,
t_qhs = t_ds,
t_qha = t_ds,
t_q = t_ds
)